OpenRaiser/Intern-Atlas huggingface dataset:
Kronecker Product Correlation Model and Limited Feedback Codebook Design in a 3D Channel Model
Venue: arXiv cs.IT 2014
Year: 2014

A 2D antenna array introduces a new level of control and additional degrees of freedom in multiple-input-multiple-output (MIMO) systems particularly for the so-called "massive MIMO" systems. To accurately assess the performance gains of these large arrays, existing azimuth-only channel models have been extended to handle 3D channels by modeling both the elevation and azimuth dimensions. In this paper, we study the channel correlation matrix of a generic ray-based 3D channel model, and our analysis and simulation results demonstrate that the 3D correlation matrix can be well approximated by a Kronecker production of azimuth and elevation correlations. This finding lays the theoretical support for the usage of a product codebook for reduced complexity feedback from the receiver to the transmitter. We also present the design of a product codebook based on Grassmannian line packing.
A Hypermedia Distributed Application for Monitoring and Fault-Injection in Embedded Fault-tolerant Parallel Programs
Venue: arXiv cs.DC 2014
Year: 2014

We describe a distributed, multimedia application which is being developed in the framework of the ESPRIT-IV Project 21012 EFTOS (Embedded Fault-Tolerant Supercomputing). The application dynamically sets up a hierarchy of HTML pages reflecting the current status of an EFTOS-compliant dependable application running on a Parsytec CC system. These pages are fed to a World-Wide Web browser playing the role of a hypermedia monitor. The adopted approach allows the user to concentrate on the high-level aspects of his/her application so to quickly assess the quality of its current fault-tolerance design. This view of the system lends itself well for being coupled with a tool to interactively inject software faults in the user application; this tool is currently under development.
On the Secrecy Outage Capacity of Physical Layer Security in Large-Scale MIMO Relaying Systems with Imperfect CSI
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we study the problem of physical layer security in a large-scale multiple-input multiple-output (LS-MIMO) relaying system. The advantage of LS-MIMO relaying systems is exploited to enhance both wireless security and spectral efficiency. In particular, the challenging issue incurred by short interception distance is well addressed. Under very practical assumptions, i.e., no eavesdropper's channel state information (CSI) and imperfect legitimate channel CSI, this paper gives a thorough investigation of the impact of imperfect CSI in two classic relaying systems, i.e., amplify-and-forward (AF) and decode-and-forward (DF) systems, and obtain explicit expressions of secrecy outage capacities for both cases. Finally, our theoretical claims are validated by the numerical results.
Power of individuals -- Controlling centrality of temporal networks
Venue: arXiv cs.SI 2014
Year: 2014

Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on structural controllability of temporal networks, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability of temporal networks, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the scale-free distribution of node's controlling centrality is virtually independent of the time scale and types of datasets, meaning the inherent heterogeneity and robustness of the controlling centrality of temporal networks.
From Participatory Sensing to Mobile Crowd Sensing
Venue: arXiv cs.HC 2014
Year: 2014

The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.
Half-Duplex Relaying for the Multiuser Channel
Venue: arXiv cs.IT 2014
Year: 2014

This work focuses on studying the half-duplex (HD) relaying in the Multiple Access Relay Channel (MARC) and the Compound Multiple Access Channel with a Relay (cMACr). A generalized Quantize-and-Forward (GQF) has been proposed to establish the achievable rate regions. Such scheme is developed based on the variation of the Quantize-and-Forward (QF) scheme and single block with two slots coding structure. The results in this paper can also be considered as a significant extension of the achievable rate region of Half-Duplex Relay Channel (HDRC). Furthermore, the rate regions based on GQF scheme is extended to the Gaussian channel case. The scheme performance is shown through some numerical examples.
Hidden Attacks on Power Grid: Optimal Attack Strategies and Mitigation
Venue: arXiv cs.CR 2014
Year: 2014

Real time operation of the power grid and synchronism of its different elements require accurate estimation of its state variables. Errors in state estimation will lead to sub-optimal Optimal Power Flow (OPF) solutions and subsequent increase in the price of electricity in the market or, potentially overload and create line outages. This paper studies hidden data attacks on power systems by an adversary trying to manipulate state estimators. The adversary gains control of a few meters, and is able to introduce spurious measurements in them. The paper presents a polynomial time algorithm using min-cut calculations to determine the minimum number of measurements an adversary needs to manipulate in order to perform a hidden attack. Greedy techniques are presented to aid the system operator in identifying critical measurements for protection to prevent such hidden data attacks. Secure PMU placement against data attacks is also discussed and an algorithm for placing PMUs for this purpose is developed. The performances of the proposed algorithms are shown through simulations on IEEE test cases.
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Venue: arXiv cs.IT 2014
Year: 2014

Predictive coding is attractive for compression onboard of spacecrafts thanks to its low computational complexity, modest memory requirements and the ability to accurately control quality on a pixel-by-pixel basis. Traditionally, predictive compression focused on the lossless and near-lossless modes of operation where the maximum error can be bounded but the rate of the compressed image is variable. Rate control is considered a challenging problem for predictive encoders due to the dependencies between quantization and prediction in the feedback loop, and the lack of a signal representation that packs the signal's energy into few coefficients. In this paper, we show that it is possible to design a rate control scheme intended for onboard implementation. In particular, we propose a general framework to select quantizers in each spatial and spectral region of an image so as to achieve the desired target rate while minimizing distortion. The rate control algorithm allows to achieve lossy, near-lossless compression, and any in-between type of compression, e.g., lossy compression with a near-lossless constraint. While this framework is independent of the specific predictor used, in order to show its performance, in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless compression standard, obtaining an extension that allows to perform lossless, near-lossless and lossy compression in a single package. We show that the rate controller has excellent performance in terms of accuracy in the output rate, rate-distortion characteristics and is extremely competitive with respect to state-of-the-art transform coding.
Groupoid Semantics for Thermal Computing
Venue: arXiv cs.LO 2014
Year: 2014

A groupoid semantics is presented for systems with both logical and thermal degrees of freedom. We apply this to a syntactic model for encryption, and obtain an algebraic characterization of the heat produced by the encryption function, as predicted by Landauer's principle. Our model has a linear representation theory that reveals an underlying quantum semantics, giving for the first time a functorial classical model for quantum teleportation and other quantum phenomena.
Homomorphisms of signed planar graphs
Venue: arXiv cs.DM 2014
Year: 2014

Signed graphs are studied since the middle of the last century. Recently, the notion of homomorphism of signed graphs has been introduced since this notion captures a number of well known conjectures which can be reformulated using the definitions of signed homomorphism. In this paper, we introduce and study the properties of some target graphs for signed homomorphism. Using these properties, we obtain upper bounds on the signed chromatic numbers of graphs with bounded acyclic chromatic number and of signed planar graphs with given girth.
A programme to determine the exact interior of any connected digital picture
Venue: arXiv cs.CG 2014
Year: 2014

Region filling is one of the most important and fundamental operations in computer graphics and image processing. Many filling algorithms and their implementations are based on the Euclidean geometry, which are then translated into computational models moving carelessly from the continuous to the finite discrete space of the computer. The consequences of this approach is that most implementations fail when tested for challenging degenerate and nearly degenerate regions. We present a correct integer-only procedure that works for all connected digital pictures. It finds all possible interior points, which are then displayed and stored in a locating matrix. Namely, we present a filling and locating procedure that can be used in computer graphics and image processing applications.
Maximum Throughput of a Cooperative Energy Harvesting Cognitive Radio User
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we investigate the maximum throughput of a saturated rechargeable secondary user (SU) sharing the spectrum with a primary user (PU). The SU harvests energy packets (tokens) from the environment with a certain harvesting rate. All transmitters are assumed to have data buffers to store the incoming data packets. In addition to its own traffic buffer, the SU has a buffer for storing the admitted primary packets for relaying; and a buffer for storing the energy tokens harvested from the environment. We propose a new cooperative cognitive relaying protocol that allows the SU to relay a fraction of the undelivered primary packets. We consider an interference channel model (or a multipacket reception (MPR) channel model), where concurrent transmissions can survive from interference with certain probability characterized by the complement of channel outages. The proposed protocol exploits the primary queue burstiness and receivers' MPR capability. In addition, it efficiently expends the secondary energy tokens under the objective of secondary throughput maximization. Our numerical results show the benefits of cooperation, receivers' MPR capability, and secondary energy queue arrival rate on the system performance from a network layer standpoint.
Effect of ISI Mitigation on Modulation Techniques in Communication via Diffusion
Venue: arXiv cs.ET 2014
Year: 2014

Communication via diffusion (CvD) is an effective and energy efficient method for transmitting information in nanonetworks. In this work, we focus on a diffusion-based communication system where the reception process is an absorption via receptors. Whenever a molecule hits to the receiver it is removed from the environment. This kind of reception process is called first passage process and it is more complicated compared to diffusion process only. In 3-D environments, obtaining analytical solution for hitting time distribution for realistic cases is complicated, hence we develop an end-to-end simulator for he diffusion-based communication system that sends consecutive symbols. In CvD, each symbol is modulated and demodulated in a time slot called symbol duration, however the long tail distribution of hitting time is the main challenge that affects the symbol detection error. The molecules arriving in the following slots become an interference source when detection takes place. End-to-end simulator enables us to analyze the effect of inter symbol interference (ISI) without making any assumptions on the ISI. We propose an ISI cancellation technique that utilizes decision feedback for compensating the effect of previously demodulated symbol. Three different modulation types are considered with pulse, square, and cosine carrier waves. In case of constraints on transmitter or receiver node it may not be possible to use pulse as a carrier, and peak-to-average messenger molecule metric is defined for this purpose. Results show that, the proposed ISI mitigation technique improves the symbol detection performance and the amplitude-based modulations are improved more than frequency-based modulations.
Networks of Influence Diagrams: A Formalism for Representing Agents' Beliefs and Decision-Making Processes
Venue: arXiv cs.GT 2014
Year: 2014

This paper presents Networks of Influence Diagrams (NID), a compact, natural and highly expressive language for reasoning about agents beliefs and decision-making processes. NIDs are graphical structures in which agents mental models are represented as nodes in a network; a mental model for an agent may itself use descriptions of the mental models of other agents. NIDs are demonstrated by examples, showing how they can be used to describe conflicting and cyclic belief structures, and certain forms of bounded rationality. In an opponent modeling domain, NIDs were able to outperform other computational agents whose strategies were not known in advance. NIDs are equivalent in representation to Bayesian games but they are more compact and structured than this formalism. In particular, the equilibrium definition for NIDs makes an explicit distinction between agents optimal strategies, and how they actually behave in reality.
A Unifying Framework for Structural Properties of CSPs: Definitions, Complexity, Tractability
Venue: arXiv cs.AI 2014
Year: 2014

Literature on Constraint Satisfaction exhibits the definition of several structural properties that can be possessed by CSPs, like (in)consistency, substitutability or interchangeability. Current tools for constraint solving typically detect such properties efficiently by means of incomplete yet effective algorithms, and use them to reduce the search space and boost search. In this paper, we provide a unifying framework encompassing most of the properties known so far, both in CSP and other fields literature, and shed light on the semantical relationships among them. This gives a unified and comprehensive view of the topic, allows new, unknown, properties to emerge, and clarifies the computational complexity of the various detection problems. In particular, among the others, two new concepts, fixability and removability emerge, that come out to be the ideal characterisations of values that may be safely assigned or removed from a variables domain, while preserving problem satisfiability. These two notions subsume a large number of known properties, including inconsistency, substitutability and others. Because of the computational intractability of all the property-detection problems, by following the CSP approach we then determine a number of relaxations which provide sufficient conditions for their tractability. In particular, we exploit forms of language restrictions and local reasoning.
Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach
Venue: arXiv cs.LG 2014
Year: 2014

Machine learning techniques are gaining prevalence in the production of a wide range of classifiers for complex real-world applications with nonuniform testing and misclassification costs. The increasing complexity of these applications poses a real challenge to resource management during learning and classification. In this work we introduce ACT (anytime cost-sensitive tree learner), a novel framework for operating in such complex environments. ACT is an anytime algorithm that allows learning time to be increased in return for lower classification costs. It builds a tree top-down and exploits additional time resources to obtain better estimations for the utility of the different candidate splits. Using sampling techniques, ACT approximates the cost of the subtree under each candidate split and favors the one with a minimal cost. As a stochastic algorithm, ACT is expected to be able to escape local minima, into which greedy methods may be trapped. Experiments with a variety of datasets were conducted to compare ACT to the state-of-the-art cost-sensitive tree learners. The results show that for the majority of domains ACT produces significantly less costly trees. ACT also exhibits good anytime behavior with diminishing returns.
Learning Document-Level Semantic Properties from Free-Text Annotations
Venue: arXiv cs.CL 2014
Year: 2014

This paper presents a new method for inferring the semantic properties of documents by leveraging free-text keyphrase annotations. Such annotations are becoming increasingly abundant due to the recent dramatic growth in semi-structured, user-generated online content. One especially relevant domain is product reviews, which are often annotated by their authors with pros/cons keyphrases such as a real bargain or good value. These annotations are representative of the underlying semantic properties; however, unlike expert annotations, they are noisy: lay authors may use different labels to denote the same property, and some labels may be missing. To learn using such noisy annotations, we find a hidden paraphrase structure which clusters the keyphrases. The paraphrase structure is linked with a latent topic model of the review texts, enabling the system to predict the properties of unannotated documents and to effectively aggregate the semantic properties of multiple reviews. Our approach is implemented as a hierarchical Bayesian model with joint inference. We find that joint inference increases the robustness of the keyphrase clustering and encourages the latent topics to correlate with semantically meaningful properties. Multiple evaluations demonstrate that our model substantially outperforms alternative approaches for summarizing single and multiple documents into a set of semantically salient keyphrases.
Compiling Uncertainty Away in Conformant Planning Problems with Bounded Width
Venue: arXiv cs.AI 2014
Year: 2014

Conformant planning is the problem of finding a sequence of actions for achieving a goal in the presence of uncertainty in the initial state or action effects. The problem has been approached as a path-finding problem in belief space where good belief representations and heuristics are critical for scaling up. In this work, a different formulation is introduced for conformant problems with deterministic actions where they are automatically converted into classical ones and solved by an off-the-shelf classical planner. The translation maps literals L and sets of assumptions t about the initial situation, into new literals KL/t that represent that L must be true if t is initially true. We lay out a general translation scheme that is sound and establish the conditions under which the translation is also complete. We show that the complexity of the complete translation is exponential in a parameter of the problem called the conformant width, which for most benchmarks is bounded. The planner based on this translation exhibits good performance in comparison with existing planners, and is the basis for T0, the best performing planner in the Conformant Track of the 2006 International Planning Competition.
Exploiting Single-Cycle Symmetries in Continuous Constraint Problems
Venue: arXiv cs.AI 2014
Year: 2014

Symmetries in discrete constraint satisfaction problems have been explored and exploited in the last years, but symmetries in continuous constraint problems have not received the same attention. Here we focus on permutations of the variables consisting of one single cycle. We propose a procedure that takes advantage of these symmetries by interacting with a continuous constraint solver without interfering with it. A key concept in this procedure are the classes of symmetric boxes formed by bisecting a n-dimensional cube at the same point in all dimensions at the same time. We analyze these classes and quantify them as a function of the cube dimensionality. Moreover, we propose a simple algorithm to generate the representatives of all these classes for any number of variables at very high rates. A problem example from the chemical and#64257;eld and the cyclic n-roots problem are used to show the performance of the approach in practice.
Optimal Value of Information in Graphical Models
Venue: arXiv cs.AI 2014
Year: 2014

Many real-world decision making tasks require us to choose among several expensive observations. In a sensor network, for example, it is important to select the subset of sensors that is expected to provide the strongest reduction in uncertainty. In medical decision making tasks, one needs to select which tests to administer before deciding on the most effective treatment. It has been general practice to use heuristic-guided procedures for selecting observations. In this paper, we present the first efficient optimal algorithms for selecting observations for a class of probabilistic graphical models. For example, our algorithms allow to optimally label hidden variables in Hidden Markov Models (HMMs). We provide results for both selecting the optimal subset of observations, and for obtaining an optimal conditional observation plan. Furthermore we prove a surprising result: In most graphical models tasks, if one designs an efficient algorithm for chain graphs, such as HMMs, this procedure can be generalized to polytree graphical models. We prove that the optimizing value of information is $NP^{PP}$-hard even for polytrees. It also follows from our results that just computing decision theoretic value of information objective functions, which are commonly used in practice, is a #P-complete problem even on Naive Bayes models (a simple special case of polytrees). In addition, we consider several extensions, such as using our algorithms for scheduling observation selection for multiple sensors. We demonstrate the effectiveness of our approach on several real-world datasets, including a prototype sensor network deployment for energy conservation in buildings.